Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach

Wenjie Xu, Bratislav Svetozarevic, Loris Di Natale, Philipp Heer and Colin N. Jones

Applied Energy, 2024, vol. 358, issue C, No S0306261923018573

Abstract: We study the problem of tuning the parameters of a room temperature controller to minimize its energy consumption, subject to the constraint that the daily cumulative thermal discomfort of the occupants is below a given threshold. We formulate it as an online constrained black-box optimization problem where, on each day, we observe some relevant environmental context and adaptively select the controller parameters. In this paper, we propose to use a data-driven Primal-Dual Contextual Bayesian Optimization (PDCBO) approach to solve this problem.

Keywords: Building thermal control; Controller tuning; Bayesian optimization; Contextual model; Primal–dual method (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2023.122493

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